Abstract
We investigated the signals regularity of electroencephalography (EEG) channels separately and determined the energy of selected frequency waves, such as, δ, θ, α, β, and γ. The goal of this research is to identify the prominent frequency band from selected frequencies. We recorded the EEG signal data of 30 controlled subjects with 18 EEG channels. These subjects are all males with an average age of 24 years. Emotional stimuli related to different emotions were presented to each of selected candidates. EEG data were extracted and further processed for artifact removal, filtering, epoch selection and averaging of the signals. We designed and tested our method for exploring the frequency waves of all EEG channels. We also employed the Hjorth parameters to measure the signal regularity in time and frequency domain. The detailed physiological response of human subjects is also presented in this paper. Our results showed that the energy level of delta wave is mostly high in all cases.
Highlights
Wireless sensors and human computer interaction have been involved significantly from medicine to military in recent years
Our research work provides an application of time and frequency domain analysis on EEG signal data of controlled human subjects
We successfully explored the prominent frequency band which may lead the researchers to identify the emotional behavior from human subjects
Summary
Wireless sensors and human computer interaction have been involved significantly from medicine to military in recent years. Teachers observe students’ expressions through face-to-face communication. It will help the teachers and instructors to determine the current situation of the student. Students may participate in distance learning through the Internet. This way of teaching further increases the complexity of finding the student attention remotely. The brain neurons are subjective and always active without any reason in the human brain. These neurons are producing significant amount of electromagnetic wave patterns. These signal patterns are further used as EEG signals and recorded by EEG enabled machines
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